{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 单因子数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_monthly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <td>14997</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14998</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <td>14999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999999.00</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sale</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15001</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sale</td>\n",
       "      <td>nme</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15002 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "14999                 NaN             0.52               2   \n",
       "15000                 NaN        999999.00               2   \n",
       "15001                0.70             0.40               2   \n",
       "\n",
       "       average_monthly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                        157                   3              0     1   \n",
       "1                        262                   6              0     1   \n",
       "2                        272                   4              0     1   \n",
       "3                        223                   5              0     1   \n",
       "4                        159                   3              0     1   \n",
       "...                      ...                 ...            ...   ...   \n",
       "14997                    280                   4              0     1   \n",
       "14998                    158                   3              0     1   \n",
       "14999                    158                   3              0     1   \n",
       "15000                    158                   3              0     1   \n",
       "15001                    158                   2              0     1   \n",
       "\n",
       "       promotion_last_5years department  salary  \n",
       "0                          0      sales     low  \n",
       "1                          0      sales  medium  \n",
       "2                          0      sales  medium  \n",
       "3                          0      sales     low  \n",
       "4                          0      sales     low  \n",
       "...                      ...        ...     ...  \n",
       "14997                      0    support     low  \n",
       "14998                      0    support     low  \n",
       "14999                      0    support     low  \n",
       "15000                      0       sale     low  \n",
       "15001                      0       sale     nme  \n",
       "\n",
       "[15002 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "df = pd.read_csv('./data/HR.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_monthly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
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       "      <td>low</td>\n",
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       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
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       "      <td>14995</td>\n",
       "      <td>0.37</td>\n",
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       "      <td>3</td>\n",
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       "      <td>4</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
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       "<p>14999 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_monthly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                        157                   3              0     1   \n",
       "1                        262                   6              0     1   \n",
       "2                        272                   4              0     1   \n",
       "3                        223                   5              0     1   \n",
       "4                        159                   3              0     1   \n",
       "...                      ...                 ...            ...   ...   \n",
       "14994                    151                   3              0     1   \n",
       "14995                    160                   3              0     1   \n",
       "14996                    143                   3              0     1   \n",
       "14997                    280                   4              0     1   \n",
       "14998                    158                   3              0     1   \n",
       "\n",
       "       promotion_last_5years department  salary  \n",
       "0                          0      sales     low  \n",
       "1                          0      sales  medium  \n",
       "2                          0      sales  medium  \n",
       "3                          0      sales     low  \n",
       "4                          0      sales     low  \n",
       "...                      ...        ...     ...  \n",
       "14994                      0    support     low  \n",
       "14995                      0    support     low  \n",
       "14996                      0    support     low  \n",
       "14997                      0    support     low  \n",
       "14998                      0    support     low  \n",
       "\n",
       "[14999 rows x 10 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 先剔除异常值\n",
    "df = df.dropna(axis=0,how='any')\n",
    "df = df[df['salary']!='nme']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns指定样式\n",
    "sns.set_style(style='whitegrid')\n",
    "sns.set_context(context='paper',font_scale=1.4)\n",
    "sns.set_palette(sns.color_palette('RdBu',n_colors=7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "柱状图,横坐标是类别，顶部为有意义的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制salary的柱状图,横坐标是类别，顶部为有意义的值\n",
    "plt.title('SALARY')\n",
    "plt.xlabel('salary')\n",
    "plt.ylabel('Number')\n",
    "plt.xticks(np.arange(len(df['salary'].value_counts()))+0.5,df['salary'].value_counts().index)\n",
    "plt.axis([0,4,0,10000])\n",
    "plt.bar(np.arange(len(df['salary'].value_counts()))+0.5,df['salary'].value_counts(),width=0.5)\n",
    "for x,y in zip(np.arange(len(df['salary'].value_counts()))+0.5,df['salary'].value_counts()):\n",
    "    plt.text(x,y,y,ha='center',va='bottom')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用seaborn进行绘图\n",
    "sns.countplot(x='salary',hue='department',data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "直方图，横坐标为区间，有意义的是直方图的面积"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制直方图，横坐标为区间，有意义的是直方图的面积\n",
    "f = plt.figure()\n",
    "f.add_subplot(1,3,1)\n",
    "# 禁用分布图\n",
    "# sns.distplot(df['satisfaction_level'],bins=10,kde=False)\n",
    "# 禁用直方图\n",
    "# sns.distplot(df['satisfaction_level'],bins=10,hist=False)\n",
    "sns.distplot(df['satisfaction_level'],bins=10)\n",
    "f.add_subplot(1,3,2)\n",
    "sns.distplot(df['last_evaluation'],bins=10)\n",
    "f.add_subplot(1,3,3)\n",
    "sns.distplot(df['average_monthly_hours'],bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "箱线图,展示异常值的位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制箱线图,展示异常值的位置,saturation指上四分位数，whis指间隔倍数\n",
    "sns.boxplot(x=df['time_spend_company'],saturation=0.75,whis=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "折线图，指数据变化的趋势，横轴一般是时间或者规模，通过折线图可以看到数据的走势和范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 折线图，指数据变化的趋势，横轴一般是时间或者规模，通过折线图可以看到数据的走势和范围\n",
    "# 绘制随着在公司呆的时间，离职的变化趋势\n",
    "\n",
    "# 第一种画法\n",
    "# sub_df = df.groupby('time_spend_company').mean()\n",
    "# sns.pointplot(sub_df.index,sub_df['left'])\n",
    "\n",
    "# 第二种画法\n",
    "sns.pointplot(x='time_spend_company',y='left',data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "饼图主要用来做结构分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn中没有饼图，使用plt画\n",
    "# 饼图主要用来做结构分析\n",
    "lbs = df['department'].value_counts().index\n",
    "# 把某个部门单独强调\n",
    "explodes = [0.1 if i=='sales' else 0 for i in lbs]\n",
    "plt.pie(df['department'].value_counts(normalize=True),explode=explodes,labels=lbs,autopct='%1.1f%%',colors=sns.color_palette('Reds'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lbs = df['salary'].value_counts().index\n",
    "explodes = [0.1 if i=='low' else 0 for i in lbs]\n",
    "plt.pie(df['salary'].value_counts(normalize=True),explode=explodes,labels=lbs,autopct='%1.1f%%',colors=sns.color_palette('Reds'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
